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Methods for solving equations with errors based on the HHL algorithm

The Journal of China Universities of Posts and Telecommunications 2022年第4期29卷 9-20页

作者： LüLihui Wang Hong Ma Zhi Duan Qianheng Fei Yangyang Meng Xiangdong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China

To solve polynomial systems,Harrow,Hassidim,and Lloyd(HHL)proposed a quantum algorithm called HHL *** on the HHL algorithm,Chen et *** an algorithm,the solving the Boolean solutions of polynomial systems(PoSSoB)***,Ding et *** the Boolean Macaulay matrix and analyzed the lower bound on the condition *** by Ding et al.’s research,several related algorithms are proposed in this ***,the improved PoSSoB algorithm using the Boolean Macaulay matrix is proved to have lower ***,for solving equations with errors,a quantum algorithm for the max-polynomial system solving(Max-PoSSo)problem is proposed based on the improved PoSSoB ***,the Max-PoSSo algorithm is extended to the learning with errors(LWE)problem and its special case,the learning parity with noise(LPN)problem,providing a quantitative criterion,the condition number,for the security of these basic problems.

关键词： Harrow Hassidim and Lloyd polynomial system solving max-polynomial system solving learning parity with noise learning with errors

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Network traffic classification based on multi-head attention and deep metric learning 8

Network traffic classification based on multi-head attention...

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8th International Conference on Electronic Technology and Information Science, ICETIS 2023

作者： Lv, Zhuo Lu, Bin Li, Xue Qi, Zan State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Information Engineering University Zhengzhou China

ISBN: (纸本)9781510666535

Network traffic classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories. © 2023 SPIE.

关键词： Classification (of information)

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Stance Detection for Social Text: Inference-Enhanced Multi-Task Learning with Machine-Annotated Supervision

Stance Detection for Social Text: Inference-Enhanced Multi-T...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pi, Qiankun Lu, Jicang Sun, Yepeng Fan, Qinlong Zhou, Xukun Shang, Shouxin State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Information Renmin University of China Beijing China

ISBN: (纸本)9798350368741

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model's understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model's adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model's stance discriminative ability and generalization performance. © 2025 IEEE.

关键词： argumentation corpus machine annotation multitask learning stance detection

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The Causal Reasoning Ability of Open Large Language Model: A Comprehensive and Exemplary Functional Testing 23

The Causal Reasoning Ability of Open Large Language Model: A...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Li, Shun-Hang Zhou, Gang Li, Zhi-Bo Lu, Ji-Cang Huang, Ning-Bo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (纸本)9798350319583

As the intelligent software, the development and application of large language models are extremely hot topics recently, bringing tremendous changes to general AI and software industry. Nonetheless, large language models, especially open source ones, incontrollably suffer from some potential software quality issues such as instability, inaccuracy, and insecurity, making software testing necessary. In this paper, we propose the first solution for functional testing of open large language models to check full-scene availability and conclude empirical principles for better steering large language models, particularly considering their black box and intelligence properties. Specifically, we focus on the model's causal reasoning ability, which is the core of artificial intelligence but almost ignored by most previous work. First, for comprehensive evaluation, we deconstruct the causal reasoning capability into five dimensions and summary the forms of causal reasoning task as causality identification and causality matching. Then, rich datasets are introduced and further modified to generate test cases along with different ability dimensions and task forms to improve the testing integrity. Moreover, we explore the ability boundary of open large language models in two usage modes: prompting and lightweight fine-tuning. Our work conducts comprehensive functional testing on the causal reasoning ability of open large language models, establishes benchmarks, and derives empirical insights for practical usage. The proposed testing solution can be transferred to other similar evaluation tasks as a general framework for large language models or their derivations. © 2023 IEEE.

关键词： Black-box testing

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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LENSER: A Channel state Information Based Indoor Localization Scheme for Malicious Devices 20

LENSER: A Channel State Information Based Indoor Localizatio...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ma, Yongqiang Luo, Xiangyang Li, Ruixiang Du, Shaoyong Liu, Wenyan State Key Laboratory of Mathematical Engineering and Advanced Computing Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China

ISBN: (纸本)9798350324334

Rapid and accurate localization of illicit micro-recorders, cameras, and other wireless network devices is of great significance for ensuring public privacy and security. However, existing methods are susceptible to environmental noise, have low localization accuracy, high localization time overhead, and require extensive manual involvement. To address these issues, we propose LENSER, a channel state information based indoor localization scheme for illicit wireless network devices. First, we set detection points to capture signal amplitude distribution changes during the antenna array rotation process, thus determining whether there is a line-of-sight propagation relationship between the detection point and the malicious device. Next, we identify the room containing the malicious device based on the line-of-sight propagation relationship and calibrate the measured position and estimate the direction of the malicious device using existing methods within the room. Finally, we establish a linear space based on the direction of the malicious device and propose an iterative comparison method based on received signal strength indicator to localize malicious devices accessing public Wi-Fi networks within the room. We conducted 1200 experiments in four buildings with different layouts and building materials. The results show that LENSER's average localization time is 12.5 minutes, and the average localization accuracy is 26.4 centimeters, an improvement of 11.1% to 86.1% in average localization accuracy and a reduction of 11.1% to 58.2% in average localization time compared to existing methods, such as Spotfi, Lumos, SCamF, SNOOPDOG, and Region Division. © 2023 IEEE.

关键词： Channel state information

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Towards Efficient and Privacy-Preserving Anomaly Detection of Blockchain-Based Cryptocurrency Transactions 25th

Towards Efficient and Privacy-Preserving Anomaly Detection ...

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25th International Conference on Information and Communications Security, ICICS 2023

作者： Song, Yuhan Zhu, Yuefei Wei, Fushan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

ISBN: (纸本)9789819973552

In recent years, a growing number of breaches targeting cryptocurrency exchanges have damaged the credibility of the entire cryptocurrency ecosystem. To prevent further harm, it’s crucial to detect the anomalous behaviors hidden within cryptocurrency transactions and offer predictive suggestions. However, details of transaction records must be carefully analyzed for effective detection, and this information could be exploited by adversaries to launch attacks such as de-anonymization and model interference. As a result, it is essential to prioritize privacy preservation when designing an anomaly detection system for cryptocurrency transactions. In this paper, we propose a privacy-preserving anomaly detection (PPad) scheme for cryptocurrency transactions based on a decision tree model, which achieves privacy preservation by using additively homomorphic encryption and matrix perturbation techniques. We also design and implement PPad’s underlying protocol in a cloud outsourcing environment. The correctness and privacy properties of PPad have been proven through detailed analysis. Experimental results show that our scheme can offer privacy assurance with desirable detection effectiveness and efficiency, making it suitable for real-world applications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Resolving Anonymous Nodes Based on "Watermelon Patterns" Towards Constructing Router-Level Topology in Mesh Network 29th

Resolving Anonymous Nodes Based on "Watermelon Patterns" Tow...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Li, Ruixiang Feng, Xinzhu Liu, Chong Yuan, Fuxiang Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou China Sichuan University Chengdu China

ISBN: (纸本)9783031425141

Router-level topology, portraying the connection relationship between network devices, is important for discovering key devices in network. In actual networks, there are some anonymous routers, which do not respond traceroute due to network congestion, firewall security settings, router response rate limitations, and other factors. As these anonymous routers are difficult to eliminate, it makes the constructed router-level topology huge in size. Currently, the time cost of finding key nodes is high. Resolving anonymous nodes for reducing the topology size is necessary to improve the efficiency in finding key node. Although anonymous route resolving methods have been available, they are difficult to handle two-hop anonymous route structures in mesh topologies. To this end, based on the four structures summarized in Graph Based Induction (GBI) method, we propose a method to resolve anonymous nodes based on "watermelon patterns". Combining with Kapar, an alias IP merging method, to build router-level topologies in mesh networks. We have conducted experiments in 8 cities, such as New York, Tokyo, Seoul and so on. The experimental results show that, our method can handle the local topology containing two-hop anonymous nodes. And using our method to further reduce the size of the router-level topology built using GBI and Kapar, which is conducive to improving the efficiency of discovering key nodes in the topology. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Topology

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Packet Modification and Utilization Technology of SOHO Router 2

Packet Modification and Utilization Technology of SOHO Route...

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2nd International Conference on advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2023

作者： Gai, Xianzhe Cai, Ruijie Jia, Fan Chen, Hongwei Yang, Pengfei Liu, Shengli State Key Laboratory of Mathematical Engineering and Advanced Computing 62 Science Ave. Zhengzhou450001 China

ISBN: (纸本)9781510667327

As a gateway to handle all intranet terminal traffic, SOHO router plays an important role in the network. In this paper, we introduced the security threats faced by SOHO routers, and on this basis, we propose a packet modification method of " Discard-Resend". The method does not affect the normal operation of the router, nor does it affect the normal communication between the two sides. We have done experiments on this packet modification method in several scenarios from the view of network security personnel and network attackers, and achieved the desired results. The packet modification method of " Discard-Resend" is a basic work in the security research of SOHO router, and it is of great significance. © 2023 SPIE.

关键词： Network security

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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